As the 5G architecture gains momentum,interest in 6G is growing.The proliferation of Internet of Things(IoT)devices,capable of capturing sensitive images,has increased the need for secure transmission and robust acces...As the 5G architecture gains momentum,interest in 6G is growing.The proliferation of Internet of Things(IoT)devices,capable of capturing sensitive images,has increased the need for secure transmission and robust access control mechanisms.The vast amount of data generated by low-computing devices poses a challenge to traditional centralized access control,which relies on trusted third parties and complex computations,resulting in intricate interactions,higher hardware costs,and processing delays.To address these issues,this paper introduces a novel distributed access control approach that integrates a decentralized and lightweight encryption mechanism with image transmission.This method enhances data security and resource efficiency without imposing heavy computational and network burdens.In comparison to the best existing approach,it achieves a 7%improvement in accuracy,effectively addressing existing gaps in lightweight encryption and recognition performance.展开更多
1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands ...1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands for greater flexibility and efficiency.The integration of advanced information technology facilitates smart manufacturing(SM),which optimizes production,management,and supply chains[1].展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
Frequent typhoons can significantly change the temperature,nutrient availability,and phytoplankton biomass in marginal seas.The oceanic response to typhoons is usually influenced by the features of the typhoon,among w...Frequent typhoons can significantly change the temperature,nutrient availability,and phytoplankton biomass in marginal seas.The oceanic response to typhoons is usually influenced by the features of the typhoon,among which the translational speed is critically important.By using a high resolution coupled physical-biological model,we investigated the response of the Yellow and East China seas(YECS)to two typhoons at different translational speeds,Muifa in August 2011 and Bolaven in August 2012.The model well reproduced the spatial and temporal variations of temperature,chlorophyll-a concentration over the YECS.Results show that typhoons with slower translational speeds uplift more deep water,leading to a more significant oceanic response.Divergence and convergence caused nutrient fluxes in opposite directions in the surface and bottom layers.Moreover,the nutrient flux in the bottom layer was greater than that in the surface layer.These phenomena are closely related to the spatial distribution of nutrients.Further studies show that the degree of ocean response to typhoons is highly correlated with the initial conditions of physical and biological elements of the upper ocean before the typhoon,as well as with ocean structure.Pretyphoon initial conditions of oceanic physical and ecological elements,mixed layer depth,and potential energy anomalies can all alter the degree of typhoon-induced oceanic response.This study emphasizes the important roles of the translational speed of typhoons and the initial oceanic conditions in the oceanic response to typhoons.展开更多
Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communicat...Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs.In addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.展开更多
Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary mea...Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.展开更多
The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images,particularly in challenging environments.However,exis...The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images,particularly in challenging environments.However,existing image fusion algorithms are generally suitable for normal scenes.In the hazy scene,a lot of texture information in the visible image is hidden,the results of existing methods are filled with infrared information,resulting in the lack of texture details and poor visual effect.To address the aforementioned difficulties,we propose a haze-free infrared and visible fusion method,termed HaIVFusion,which can eliminate the influence of haze and obtain richer texture information in the fused image.Specifically,we first design a scene information restoration network(SIRNet)to mine the masked texture information in visible images.Then,a denoising fusion network(DFNet)is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible.In addition,we use color consistency loss to reduce the color distortion resulting from haze.Furthermore,we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes.Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes,and achieves better quantitative results,when compared to state-ofthe-art image fusion methods,even combined with state-of-the-art dehazing methods.展开更多
Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement...Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Variations in ocean mixed layer depth(MLD)show a significant impact on energy balance in the global climate systems and marine ecosystems.At present,the accuracy of modeling MLD,especially in the region with complex o...Variations in ocean mixed layer depth(MLD)show a significant impact on energy balance in the global climate systems and marine ecosystems.At present,the accuracy of modeling MLD,especially in the region with complex ocean dynamics,remains a challenge,thus calling for an emergency using artificial intelligence approach to improve the assessment of the MLD.A novel convolutional neural network model was developed based on a dual-attention module(DA-CNN)to estimate the MLD in the Bay of Bengal(BoB)by integrating multi-source remote sensing data and Argo gridded data.Compared with the original CNN model,the DA-CNN model exhibits superior performance with notable improvements in the annual average root mean square error(RMSE)and R2 values by 13.0%and 8.4%,respectively,while more accurately capturing the seasonal variations in MLD.Moreover,the results using the DA-CNN model show minimum RMSE and maximum R2 values,in comparison to the calculation by the random forest,artificial neural network model,and the hybrid coordinate ocean model.Accordingly,our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.展开更多
The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re...The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.展开更多
As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitati...As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.展开更多
The advent of Large Language Models(LLMs)has sparked considerable interest in the medical image domain,as they can generalize to multiple tasks and offer outstanding performance.While LLMs achieve promising results,th...The advent of Large Language Models(LLMs)has sparked considerable interest in the medical image domain,as they can generalize to multiple tasks and offer outstanding performance.While LLMs achieve promising results,there is currently a lack of a comprehensive summary of medical images,making it challenging for researchers to understand the progress within this domain.To fill this gap,we make the first attempt to present a comprehensive survey for LLM on medical images.In addition,to better summarize the current progress comprehensively,we further introduce a novel x-stage tuning paradigm for summarization,including zero-stage tuning,one-stage tuning,and multi-stage tuning,offering a unified perspective on LLMs for medical images.Finally,we discuss challenges and future directions in this domain,aiming to spur more breakthroughs in the future.We hope this work can pave the way for the broad application of LLMs in medical images and provide a valuable resource for this domain.展开更多
In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to u...In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack segmentation.This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural surfaces.Firstly,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional module.Secondly,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small targets.Next,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks.Finally,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target determination.The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.展开更多
In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation system.The growth of parking lots has raised the likelihood of spontaneous vehicle combus-ti...In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation system.The growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative step.However,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection technologies.This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots.First,we develop a novel dataset to fill the gap,as there is a lack of publicly available data.This dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor conditions.Second,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing capabilities.Third,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency response.Experiments show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.展开更多
Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes.However,tra...Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes.However,transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain.Due to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many scenarios.To improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling technology.Nevertheless,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected aim.This paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction distribution.Bymitigating crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the blockchain.Therefore,the scheme can achieve a high-frequency transaction as well as a better blockchain scalability.Experiments results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.展开更多
Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across d...Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy regulations.The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations.The second technical challenge is handling the dynamic expansion of the federation without model retraining.To address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance.Based on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical challenge.FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally.The experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the federation.This opens up opportunities for cooperation between different organizations in wearable health monitoring.展开更多
Large Language Models(LLMs)are expanding their applications across various fields,including Industrial Internet of Things(IIoT),where they analyze sensor data,automate diagnostics,and enhance predictive maintenance.LL...Large Language Models(LLMs)are expanding their applications across various fields,including Industrial Internet of Things(IIoT),where they analyze sensor data,automate diagnostics,and enhance predictive maintenance.LLM inference is provided by service providers to users,with each inference request undergoing two phases:prefill and decode.Due to the autoregressive nature of generation,only one token can be produced per iteration,necessitating multiple iterations to complete a request.Typically,batch processing groups multiple requests into a single batch for inference,improving throughput and hardware utilization.However,in service systems,a fixed batch size presents challenges under fluctuating request volumes,particularly in IIoT environments,where data flow can vary significantly.Specifically,during the high-load periods,a fixed batch size may lead to underutilization of resources,while during the low-load periods,it may result in resource wastage.In this paper,we introduce FlexiDecode Scheduler(FDS)to address these challenges by dynamically adjusting the decoding batch size based on system load conditions,improving resource utilization,and reducing wait time during high-load periods.FDS prioritizes prefilling new requests to maximize decoding efficiency and employs a request output length predictor to optimize request scheduling,minimizing End-to-End(E2E)latency.Compared to virtual Large Language Model(vLLM)and Sarathi,our approach achieves a 23%and 16%reduction in E2E latency,improves actual request execution time by 34%and 15%,respectively,and increases computational utilization by 10%.展开更多
Indoor navigation systems are gaining traction due to their resistance to electromagnetic interference,abundant spectrum resources,and energy efficiency,underscoring the importance of indoor visible light positioning ...Indoor navigation systems are gaining traction due to their resistance to electromagnetic interference,abundant spectrum resources,and energy efficiency,underscoring the importance of indoor visible light positioning technology.Recent research focuses on using deep learning to enhance positioning accuracy,yet challenges remain in training costs,model efficiency,and performance in low Signal-to-Noise Ratio(SNR)scenarios.To address these issues,we propose a novel Long Short Term Memory network-Convolution Residual Network(LSTM-CRN)algorithm with a dataset construction method based on pilot extraction.Additionally,we introduce the Kolmogorov-Arnold Network(KAN)to improve accuracy under low SNR conditions.Extensive simulation results show that the network model trained on the dataset constructed by the pilot extraction method has higher localization efficiency and accuracy,especially compared with the network model trained directly using the received data to construct the dataset.The LSTM-KAN algorithm is trained on the dataset constructed by our method in this paper,and its average localization accuracy is verified to be 3.8 cm(SNR=30).It also shows better localization accuracy,efficiency,and real-time performance than existing mainstream methods under different SNR conditions,proving that this method is the state-of-the-art in the system described in this article.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants(62250410365,62071084)the Youth Program of Humanities and Social Sciences of the MoE(23YJCZH291)+1 种基金the Key Laboratory of Computing Power Network and Information Security,Ministry of Education(2023ZD02)Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/15/46.
文摘As the 5G architecture gains momentum,interest in 6G is growing.The proliferation of Internet of Things(IoT)devices,capable of capturing sensitive images,has increased the need for secure transmission and robust access control mechanisms.The vast amount of data generated by low-computing devices poses a challenge to traditional centralized access control,which relies on trusted third parties and complex computations,resulting in intricate interactions,higher hardware costs,and processing delays.To address these issues,this paper introduces a novel distributed access control approach that integrates a decentralized and lightweight encryption mechanism with image transmission.This method enhances data security and resource efficiency without imposing heavy computational and network burdens.In comparison to the best existing approach,it achieves a 7%improvement in accuracy,effectively addressing existing gaps in lightweight encryption and recognition performance.
基金supported in part by the National Natural Science Foundation of China(62293511 and 62402256)in part by the Shandong Provincial Natural Science Foundation of China(ZR2024MF100)+1 种基金in part by the Taishan Scholars Program(tsqn202408239)in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(ICT2025B13).
文摘1.Data security in smart manufacturing The global manufacturing sector is undergoing a digital transformation as traditional systems-reliant on physical assets such as raw materials and labor-struggle to meet demands for greater flexibility and efficiency.The integration of advanced information technology facilitates smart manufacturing(SM),which optimizes production,management,and supply chains[1].
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金Supported by the National Natural Science Foundation of China(Nos.42006018,42276009,42376199)the Open Fund Project of the Key Laboratory of Ocean Observation and Information of Hainan Province(No.HKLOOI-OF-2023-03)the Tianjin Natural Science Foundation(Nos.21JCYBJC00500,21JCQNJC00590)。
文摘Frequent typhoons can significantly change the temperature,nutrient availability,and phytoplankton biomass in marginal seas.The oceanic response to typhoons is usually influenced by the features of the typhoon,among which the translational speed is critically important.By using a high resolution coupled physical-biological model,we investigated the response of the Yellow and East China seas(YECS)to two typhoons at different translational speeds,Muifa in August 2011 and Bolaven in August 2012.The model well reproduced the spatial and temporal variations of temperature,chlorophyll-a concentration over the YECS.Results show that typhoons with slower translational speeds uplift more deep water,leading to a more significant oceanic response.Divergence and convergence caused nutrient fluxes in opposite directions in the surface and bottom layers.Moreover,the nutrient flux in the bottom layer was greater than that in the surface layer.These phenomena are closely related to the spatial distribution of nutrients.Further studies show that the degree of ocean response to typhoons is highly correlated with the initial conditions of physical and biological elements of the upper ocean before the typhoon,as well as with ocean structure.Pretyphoon initial conditions of oceanic physical and ecological elements,mixed layer depth,and potential energy anomalies can all alter the degree of typhoon-induced oceanic response.This study emphasizes the important roles of the translational speed of typhoons and the initial oceanic conditions in the oceanic response to typhoons.
基金supported in part by the Shandong Provincial Natural Science Foundation(ZR2021QF057)Taishan Scholars Program(tsqn202211203)+3 种基金Shandong Provincial Higher Education Youth Innovation Team Development Project(2022KJ 290)“20 New Universities”Project of Jinan City(202228077)QLU/SDAS Computer Science and Technology Fundamental Research Enhancement Program(2021JC02023)QLU/SDAS Pilot Project for Integrated Innovation of Science,Education,and Industry(2022JBZ01-01).
文摘Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs.In addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.
基金Supported by Shandong Province Key R and D Program,No.2021SFGC0504Shandong Provincial Natural Science Foundation,No.ZR2021MF079Science and Technology Development Plan of Jinan(Clinical Medicine Science and Technology Innovation Plan),No.202225054.
文摘Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.
基金supported by the Natural Science Foundation of Shandong Province,China(ZR2022MF237)the National Natural Science Foundation of China Youth Fund(62406155)the Major Innovation Project(2023JBZ02)of Qilu University of Technology(Shandong Academy of Sciences).
文摘The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images,particularly in challenging environments.However,existing image fusion algorithms are generally suitable for normal scenes.In the hazy scene,a lot of texture information in the visible image is hidden,the results of existing methods are filled with infrared information,resulting in the lack of texture details and poor visual effect.To address the aforementioned difficulties,we propose a haze-free infrared and visible fusion method,termed HaIVFusion,which can eliminate the influence of haze and obtain richer texture information in the fused image.Specifically,we first design a scene information restoration network(SIRNet)to mine the masked texture information in visible images.Then,a denoising fusion network(DFNet)is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible.In addition,we use color consistency loss to reduce the color distortion resulting from haze.Furthermore,we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes.Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes,and achieves better quantitative results,when compared to state-ofthe-art image fusion methods,even combined with state-of-the-art dehazing methods.
基金supported by the Major Innovation Project for the Integration of Science,Education,and Industry of Qilu University of Technology(Shandong Academy of Sciences)(Nos.2023HYZX01,2023JBZ02)the Open Project of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)(No.2023ZD007)+2 种基金the Talent Research Projects of Qilu University of Technology(Shandong Academy of Sciences)(No.2023RCKY136)the Technology and Innovation Major Project of the Ministry of Science and Technology of China(No.2022ZD0118600)the Jinan‘20 New Colleges and Universities’Funded Project(No.202333043)。
文摘Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
基金Supported by the Ministry of Science and Technology of the People’s Republic of China(No.2019 YFE 0125000)the National Natural Science Foundation of China(No.42376032)。
文摘Variations in ocean mixed layer depth(MLD)show a significant impact on energy balance in the global climate systems and marine ecosystems.At present,the accuracy of modeling MLD,especially in the region with complex ocean dynamics,remains a challenge,thus calling for an emergency using artificial intelligence approach to improve the assessment of the MLD.A novel convolutional neural network model was developed based on a dual-attention module(DA-CNN)to estimate the MLD in the Bay of Bengal(BoB)by integrating multi-source remote sensing data and Argo gridded data.Compared with the original CNN model,the DA-CNN model exhibits superior performance with notable improvements in the annual average root mean square error(RMSE)and R2 values by 13.0%and 8.4%,respectively,while more accurately capturing the seasonal variations in MLD.Moreover,the results using the DA-CNN model show minimum RMSE and maximum R2 values,in comparison to the calculation by the random forest,artificial neural network model,and the hybrid coordinate ocean model.Accordingly,our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金supported by the National Natural Science Foundation of China under Grant 62471493 and 62402257partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066 and 2023QF025+2 种基金partially supported by the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-12)partially supported by the Foundation of Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University),the Ministry of Education(No.EIN2024C006)partially supported by the Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE(No.202306).
文摘As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.
基金supported by the National Natural Science Foundation of China(NSFC)(No.62306342)the Excellent Young Scientists Fund in Hunan Province(No.2024JJ4070)+2 种基金the Science and Technology Innovation Program of Hunan Province(No.2024RC3024)the CCFZhipu.AI Large Model Innovation Fund(No.CCFZhipu202406)and the Key Laboratory of Computing Power Network and Information Security Affiliated with Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)(No.2023ZD032).
文摘The advent of Large Language Models(LLMs)has sparked considerable interest in the medical image domain,as they can generalize to multiple tasks and offer outstanding performance.While LLMs achieve promising results,there is currently a lack of a comprehensive summary of medical images,making it challenging for researchers to understand the progress within this domain.To fill this gap,we make the first attempt to present a comprehensive survey for LLM on medical images.In addition,to better summarize the current progress comprehensively,we further introduce a novel x-stage tuning paradigm for summarization,including zero-stage tuning,one-stage tuning,and multi-stage tuning,offering a unified perspective on LLMs for medical images.Finally,we discuss challenges and future directions in this domain,aiming to spur more breakthroughs in the future.We hope this work can pave the way for the broad application of LLMs in medical images and provide a valuable resource for this domain.
基金This research was funded by the National Key R&D Program of China(Project:Key Technologies and Equipment for Multi-View Stereoscopic Disaster Detection and Emergency Response to Derived Disasters in Underground Spaces,2022YFC3005600)the National Natural Science Foundation of China(52378402)+2 种基金Shandong Provincial Natural Science Foundation Youth Project(ZR2022QE021 and ZR202211100077)Shandong Province Higher Education Young Innovative Team Project(2022KJ037)State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,Jianghan University(PBSKL2022C03),funding from Shandong Railway Investment Holding Group Co.,Ltd.(“Key Technologies for Rapid and Intelligent Construction of Large Section High-Speed Railway Tunnels in Low Mountain and Hilly Areas”and“Intelligent Construction Trolley Equipment and Key Technologies for the Lining of Ultra-Long Open Tunnel Sections”).
文摘In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack segmentation.This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural surfaces.Firstly,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional module.Secondly,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small targets.Next,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks.Finally,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target determination.The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.
基金This work was supported byNatural Science Foundation of China(No.62362008,author Z.Z,https://www.nsfc.gov.cn/)Guizhou Provincial Science and Technology Projects(No.ZK[2022]149,author Z.Z,https://kjt.guizhou.gov.cn/)+2 种基金Guizhou Provincial Research Project(Youth)for Universities(No.[2022]104,author Z.Z,https://jyt.guizhou.gov.cn/)Natural Science Special Foundation of Guizhou University(No.[2021]47,author Z.Z,https://www.gzu.edu.cn/)GZU Cultivation Project of NSFC(No.[2020]80,author Z.Z,https://www.gzu.edu.cn/).
文摘In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation system.The growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative step.However,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection technologies.This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots.First,we develop a novel dataset to fill the gap,as there is a lack of publicly available data.This dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor conditions.Second,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing capabilities.Third,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency response.Experiments show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.
基金supported by Shandong Provincial Key Research and Development Program of China(2021CXGC010107,2020CXGC010107)the Shandong Provincial Natural Science Foundation of China(ZR2020KF035)the New 20 Project of Higher Education of Jinan,China(202228017).
文摘Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes.However,transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain.Due to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many scenarios.To improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling technology.Nevertheless,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected aim.This paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction distribution.Bymitigating crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the blockchain.Therefore,the scheme can achieve a high-frequency transaction as well as a better blockchain scalability.Experiments results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.
基金supported by the National Natural Science Foundation of China under Grant Nos.62002187,62002189,61972383,61972237 and 61976127the Science Research Project of Hebei Education Department of China under Grant No.QN2023184。
文摘Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy regulations.The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations.The second technical challenge is handling the dynamic expansion of the federation without model retraining.To address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance.Based on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical challenge.FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally.The experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the federation.This opens up opportunities for cooperation between different organizations in wearable health monitoring.
基金supported by the National Science and Technology Major Project(No.2022ZD0116800)the NSFC International Young Scientists Fund(No.62350410478)+2 种基金the Taishan Scholars Program(Nos.TSQNZ20230621 and TSQN202211214)the Shandong Excellent Young Scientists Fund(Overseas)(No.2023HWYQ-113)the Shandong Provincial Natural Science Foundation(No.ZR20221150015).
文摘Large Language Models(LLMs)are expanding their applications across various fields,including Industrial Internet of Things(IIoT),where they analyze sensor data,automate diagnostics,and enhance predictive maintenance.LLM inference is provided by service providers to users,with each inference request undergoing two phases:prefill and decode.Due to the autoregressive nature of generation,only one token can be produced per iteration,necessitating multiple iterations to complete a request.Typically,batch processing groups multiple requests into a single batch for inference,improving throughput and hardware utilization.However,in service systems,a fixed batch size presents challenges under fluctuating request volumes,particularly in IIoT environments,where data flow can vary significantly.Specifically,during the high-load periods,a fixed batch size may lead to underutilization of resources,while during the low-load periods,it may result in resource wastage.In this paper,we introduce FlexiDecode Scheduler(FDS)to address these challenges by dynamically adjusting the decoding batch size based on system load conditions,improving resource utilization,and reducing wait time during high-load periods.FDS prioritizes prefilling new requests to maximize decoding efficiency and employs a request output length predictor to optimize request scheduling,minimizing End-to-End(E2E)latency.Compared to virtual Large Language Model(vLLM)and Sarathi,our approach achieves a 23%and 16%reduction in E2E latency,improves actual request execution time by 34%and 15%,respectively,and increases computational utilization by 10%.
基金supported by the National Key R&D Program of China(No.2023YFB3107303)the National Natural Science Foundation of China(No.62172244)+3 种基金the Young Innovation Team of Colleges and Universities in Shandong Province(No.2021KJ001)the Taishan Scholars Program(No.TSQNZ20230621)the Shandong Provincial Natural Science Foundation(No.ZR202211150015)the“20 New Universities”Project of Jinan City(No.202333045).
文摘Indoor navigation systems are gaining traction due to their resistance to electromagnetic interference,abundant spectrum resources,and energy efficiency,underscoring the importance of indoor visible light positioning technology.Recent research focuses on using deep learning to enhance positioning accuracy,yet challenges remain in training costs,model efficiency,and performance in low Signal-to-Noise Ratio(SNR)scenarios.To address these issues,we propose a novel Long Short Term Memory network-Convolution Residual Network(LSTM-CRN)algorithm with a dataset construction method based on pilot extraction.Additionally,we introduce the Kolmogorov-Arnold Network(KAN)to improve accuracy under low SNR conditions.Extensive simulation results show that the network model trained on the dataset constructed by the pilot extraction method has higher localization efficiency and accuracy,especially compared with the network model trained directly using the received data to construct the dataset.The LSTM-KAN algorithm is trained on the dataset constructed by our method in this paper,and its average localization accuracy is verified to be 3.8 cm(SNR=30).It also shows better localization accuracy,efficiency,and real-time performance than existing mainstream methods under different SNR conditions,proving that this method is the state-of-the-art in the system described in this article.
